Daniel Demmler

Daniel Demmler is an Assistant Professor at Darmstadt University of Technology's Department of Computer Science, specializing in privacy-preserving protocols and cryptographic systems. His research focuses on practical implementations of secure multi-party computation, homomorphic encryption, and privacy-preserving machine learning frameworks.

Dr. Demmler's primary research interests lie in making cryptographic protocols practical for real-world applications. His work spans secure multi-party computation, threshold homomorphic encryption, federated learning security, and defenses against property inference attacks. He has made significant contributions to frameworks like MOTION for mixed-protocol computation and Noah's Ark for threshold-FHE systems. His research bridges theoretical cryptography with practical implementation challenges, focusing on efficiency and real-world applicability.

Analysis of his recent publications reveals a strong focus on threshold cryptography and privacy-preserving machine learning. His work shows increasing sophistication in balancing security guarantees with computational efficiency, particularly in distributed settings. The trend indicates growing interest in quantum-resistant cryptographic approaches and defenses against emerging machine learning privacy threats.

• Best Paper Award at SECRYPT 2021
• Distinguished Paper Award at CCS 2018

Dr. Demmler leads the Cryptology and Privacy Research Group at TU Darmstadt, collaborating extensively with international researchers in the field. His team focuses on developing practical implementations of advanced cryptographic protocols that can be deployed in real-world systems while maintaining strong security guarantees. Current projects include threshold homomorphic encryption systems and privacy-preserving machine learning frameworks.